Exploring the optimization of process parameters for multi-quality characteristics of metal shafts in metal injection molding using a mold flow analysis software and the MAIRCA-GRA-TOPSIS method

If there is insufficient process parameter information, highly subjective and difficult-to-quantify index weight allocation and conflicting multiple quality characteristics (MQCs) for metal injection molding (MIM), this study optimizes the metal shafts of laptops using the multi-attributive ideal-real comparative analysis (MAIRCA) weighting method and a hybrid grey relational analysis-ideal solution similarity ranking method (GRA-TOPSIS). Analysis using a comprehensive evaluation index system for the MQCs (air traps, warpage, and shear stress) of the metal shafts is used to determine the combined weight values using entropy and criteria importance and intercriteria correlation (CRITIC). Grey relational analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS) are used with Moldex3D software to verify the MQCs for the metal shafts using process parameters (injection temperature (A), injection speed (B), holding pressure (C), and mold temperature (D)) to determine the optimal combination. Simulation results show that the optimized process parameters decrease air traps by 79.17%, warpage by 10.75% and shear stress by 25.70%. The ranking results for the hybrid GRA-TOPSIS method using different weights are consistent and discrimination is better than that for the GRA or TOPSIS methods.